Extract information embedded in clinical texts, medical reports, and patient records.
Incorporating automated and manual NLP methods into healthcare text annotation workflows to gain meaningful insights from doctor's notes, lab reports, labels, etc.
Make machines understand complex texts and extract valuable insights from unstructured medical data. We have language and subject matter experts to annotate medical text data in 100+ languages and dialects accurately.Our Medical Text Annotation Capabilities
Healthcare providers can gain valuable insight from EMRs, clinical reports, and summaries. Our NLP experts can extract text from EMRs to be used in disease registries, clinical trials, and audits of healthcare services.
Clinical named entity recognition is a crucial application of medical text annotation. Let us help you extract the fundamental concepts called named entities from the medical research records, such as disease names, medication names, and lab tests.
Engage our NLP experts to mitigate the key challenge of BNER. A big part of our medical text annotation service is to extract and identify entities from biomedical instances, like chemical compounds, genes, proteins, viruses, disorders, and DNAs.
Get structured or unstructured medical data de-identified in compliance with HIPAA, GDPR, or custom needs. We can de-identify/mask patient ID, name, email, patient ID, SSN, credit card, address, phone, racial or ethnic origin, religion, political affiliation, biometric info, sexual orientation, and more.
Medical text annotation captures structured inputs from unstructured medical text. Medical transcription works by efficiently and accurately converting dedicated medical records from doctors and healthcare professionals into documents.These records may include clinical notes of patient’s history and physical reports, operative and consultation reports, clinic notes, discharge summaries, lab results, psychiatric evaluations, x-ray and pathology reports, and more. The process comprises identifying medical entities such as medications, procedures, symptoms, diseases, body region, and linking them to standard medical ontologies (e.g. UMLS, ICD-10, SNOMED).
Cogito Tech handles it via receiving digital systems, tapes, voice files, and transcribing them using headphones, a foot pedal for playback control, and a word processing program. We have a team of proficient listeners, language experts, a team with a good understanding of medical terminology, and efficiency in computer skills is essential. Medical transcriptionists rely on different resources such as dictionaries for medical definitions, speciality terms, medications, abbreviations, laboratory, and surgical terminology. We also keep our resources updated with specialized word lists, new medications, and terminologies.
Medical text annotation supports NLP (Natural Language Processing) models to extract structured insights from unstructured medical text. It helps in the following ways:-
Overall, medical text annotation supports clinical research, automated coding, decision-making, and better patient care.
In medical text annotation, different entity types are labeled to shape raw clinical data for AI and analytics. It includes the following:-
We support a range of formats including JSON, COCO JSONs, YOLO, Pascal VOC XMLs, CoNLL, and custom schema required for EHR systems or NLP pipelines.
Domain and subject matter experts (SMEs) such as medical coders, clinical researchers, and physicians play a vital role in reviewing and validating annotations to determine contextual relevance, clinical accuracy, and compliance with regulatory standards. They maintain medical datasets integrity and boost model performance by aligning with real-world clinical scenarios.
Cogito Tech adheres to GDPR, HIPAA, and ISO 27001 standards. All text annotation projects are carried out in a secure and safe environment with encrypted data transfer and role-oriented access controls.
We offer multilingual annotation support in more than 35 languages including Spanish, English, German, French, and several Asian and European languages with native and medically fluent annotators.
We address ambiguity in clinical text through a structured and collaborative approach with the involvement of expert reviewers, annotators, and subject matter experts (SMEs). Custom annotation guidelines, consensus-driven reviews, and iterative feedback loops ensure regular interpretation of complex medical language, minimizing errors and improving overall annotation quality.
To ensure accurate medical text annotation, subject matter and NLP experts at Cogito work in tandem to understand complex vocabularies and ontologies in the healthcare domain and work in close collaboration to accurately label medical data.
Subject matter experts with years of experience in medical data annotation ensure accurately labelled datasets
Ensure compliance with HIPAA by maintaining strict data security and confidentiality policies
we provide fast and accurate annotations through manual and AI-based methods with 1500+ data and industry experts on board
Data is audited and reviewed multiple times to ensure it is of the highest quality and accuracy possible
With experts having specialized knowledge and an established track record of thousands of successful accomplishments, Cogito stands out as a worthy data partner to get on board for the success of your machine-learning model.